fastdev.nn.pointnet
===================

.. py:module:: fastdev.nn.pointnet

.. autoapi-nested-parse::

   PointNet implementation in PyTorch.

   Adapted from: https://github.com/yanx27/Pointnet_Pointnet2_pytorch/







Module Contents
---------------

.. py:class:: PointNetEncoder(input_dim: int = 3, feature_dim: int = 1024, global_feature: bool = True, point_transform: bool = False, feature_transform: bool = False, pretrained_filename: Optional[Literal['models/pointnn/pointnet_cls_241031.safetensors']] = None)

   Bases: :py:obj:`torch.nn.Module`


   Encoder for PointNet.

   This implementation differs from the original repository by adding an option to apply a transformation to the input points (default: False).
   STNkd is reused for both point and feature transformations, with STN3d being a special case of STNkd.

   Forward method:
       Args:
           x (torch.Tensor): Input tensor of shape (batch_size, channels, num_points).
       Returns:
           torch.Tensor: Extracted features.
           torch.Tensor: Transformation matrix applied to the points.
           torch.Tensor: Transformed features.

   Reference: https://github.com/yanx27/Pointnet_Pointnet2_pytorch/blob/master/models/pointnet_utils.py


   .. py:attribute:: global_feat
      :value: True



   .. py:attribute:: point_transform
      :value: False



   .. py:attribute:: feature_transform
      :value: False



   .. py:attribute:: conv1


   .. py:attribute:: conv2


   .. py:attribute:: conv3


   .. py:attribute:: bn1


   .. py:attribute:: bn2


   .. py:attribute:: bn3


   .. py:attribute:: relu


   .. py:method:: forward(x: torch.Tensor)

      Forward pass.

      :param x: Input tensor of shape (batch_size, channels, num_points).
      :type x: torch.Tensor

      :returns: Extracted features.
                torch.Tensor: Transformation matrix applied to the points.
                torch.Tensor: Transformation matrix applied to the features.
      :rtype: torch.Tensor



.. py:class:: PointNetCls(k: int = 40, normal_channel: bool = True, point_transform: bool = False, feature_transform: bool = False, pretrained_filename: Optional[Literal['models/pointnn/pointnet_cls_241031.safetensors']] = None)

   Bases: :py:obj:`torch.nn.Module`


   .. py:attribute:: feat


   .. py:attribute:: fc1


   .. py:attribute:: fc2


   .. py:attribute:: fc3


   .. py:attribute:: dropout


   .. py:attribute:: bn1


   .. py:attribute:: bn2


   .. py:attribute:: relu


   .. py:attribute:: log_softmax


   .. py:method:: forward(x)


.. py:function:: feature_transform_reguliarzer(trans: torch.Tensor) -> torch.Tensor

.. py:class:: PointNetSemSeg(num_classes: int, point_transform: bool = False, feature_transform: bool = False)

   Bases: :py:obj:`torch.nn.Module`


   .. py:attribute:: k


   .. py:attribute:: feat


   .. py:attribute:: conv1


   .. py:attribute:: conv2


   .. py:attribute:: conv3


   .. py:attribute:: conv4


   .. py:attribute:: bn1


   .. py:attribute:: bn2


   .. py:attribute:: bn3


   .. py:attribute:: relu


   .. py:attribute:: log_softmax


   .. py:method:: forward(x)